{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 聚类模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#导入相关模块\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "   Unnamed: 0    event_id     user_id                start_time city state  \\\n",
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       "\n",
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取从DataMining中得到的csv文件并显示相关信息\n",
    "events_final = pd.read_csv(\"C:\\\\Users\\\\my\\\\AI assignments\\\\Week_4\\\\union_events_saved.csv\")\n",
    "events_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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       "<p>13418 rows × 101 columns</p>\n",
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       "...              ...       ...       ...       ...       ...       ...   \n",
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       "3499143688  0.000000  0.000000  0.000000       0.0  0.000000  0.000000   \n",
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       "2795929444  0.022311  0.000000  0.026978       0.0  0.000000  0.000000   \n",
       "414015310   0.000000  0.000000  0.000000       0.0  0.000000  0.000000   \n",
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       "2660812160  0.000000  0.000000  0.000000       0.0  0.000000  0.000000   \n",
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       "2736696425  0.000000  0.000000  0.000000       0.0  0.000000  0.000000   \n",
       "\n",
       "            count_99  count_100  count_other  \n",
       "684921758   0.000000   0.000000     0.000622  \n",
       "244999119   0.000000   0.000000     0.000484  \n",
       "3928440935  0.000000   0.000000     0.000829  \n",
       "2582345152  0.000000   0.000000     0.000553  \n",
       "1051165850  0.000000   0.000000     0.000622  \n",
       "1212611096  0.000000   0.000000     0.001520  \n",
       "3689283674  0.000000   0.000000     0.001934  \n",
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       "3365728297  0.018696   0.000000     0.001727  \n",
       "2912638473  0.000000   0.000000     0.000207  \n",
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       "1304227508  0.000000   0.000000     0.000622  \n",
       "2608543989  0.000000   0.000000     0.002487  \n",
       "298169907   0.000000   0.024961     0.001036  \n",
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       "615449287   0.000000   0.000000     0.000622  \n",
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       "2259674237  0.000000   0.000000     0.000415  \n",
       "...              ...        ...          ...  \n",
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       "3841242017  0.000000   0.000000     0.000138  \n",
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       "3572977792  0.000000   0.000000     0.004560  \n",
       "3781269679  0.000000   0.000000     0.006080  \n",
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       "2914345387  0.000000   0.000000     0.001658  \n",
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       "3962281765  0.000000   0.000000     0.004076  \n",
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       "3002725183  0.000000   0.000000     0.001105  \n",
       "2249264436  0.000000   0.000000     0.001174  \n",
       "1831967705  0.000000   0.000000     0.025286  \n",
       "1492629072  0.018696   0.024961     0.008360  \n",
       "2359252504  0.000000   0.000000     0.014439  \n",
       "3499143688  0.000000   0.000000     0.000276  \n",
       "695330828   0.000000   0.000000     0.000622  \n",
       "2795929444  0.000000   0.024961     0.005734  \n",
       "414015310   0.000000   0.000000     0.002004  \n",
       "2559530421  0.000000   0.024961     0.004491  \n",
       "1494513322  0.000000   0.000000     0.001934  \n",
       "2660812160  0.000000   0.000000     0.001244  \n",
       "1889561284  0.000000   0.000000     0.000276  \n",
       "2738205241  0.000000   0.000000     0.000484  \n",
       "3409015015  0.000000   0.000000     0.000691  \n",
       "3119357029  0.000000   0.000000     0.000415  \n",
       "2736696425  0.000000   0.000000     0.009120  \n",
       "\n",
       "[13418 rows x 101 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#提取目标项目的关键特征作聚类，按照作业要求，提取count_1, count_2, ..., count_other属性，并对其作正则化。\n",
    "df = events_final.iloc[:,10:]\n",
    "from sklearn.preprocessing import normalize\n",
    "df = normalize(df, norm = \"l2\", axis =0, copy=False)\n",
    "columns = list('count_'+str(x) for x in range(1,101))\n",
    "columns.append('count_other')\n",
    "index = np.array(events_final['event_id'])\n",
    "#用DataFrame格式显示出需要聚类的events及其经normalize后的特征值\n",
    "pd.DataFrame(df,index = index,columns = columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按作业要求，利用KMeans模型对数据进行拟合并进行评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#利用KMeans模块作聚类分析，先定义一个函数，函数内容包括导入模型，对数据进行训练并对训练结果进行评估，返回评估值\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "def K_cluster_analysis(K, df):\n",
    "    print(\"K-means begin with clusters: {}\".format(K));\n",
    "    \n",
    "    #K-means,在训练集上训练\n",
    "    km = KMeans(n_clusters = K)\n",
    "    km.fit(df)\n",
    "    \n",
    "    #保存预测结果\n",
    "    cluster_result = km.predict(df)\n",
    "\n",
    "    # 不同K值下得到的模型评估指标\n",
    "    SC_score = metrics.silhouette_score(df,cluster_result)   \n",
    "    print(\"SC_score based on KMeans Model: {}\".format(SC_score))\n",
    "\n",
    "    return SC_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-means begin with clusters: 5\n",
      "SC_score based on KMeans Model: 0.28043087160230423\n",
      "K-means begin with clusters: 10\n",
      "SC_score based on KMeans Model: 0.22912911553595794\n",
      "K-means begin with clusters: 20\n",
      "SC_score based on KMeans Model: 0.1964273910566816\n",
      "K-means begin with clusters: 30\n",
      "SC_score based on KMeans Model: 0.1890744478349038\n",
      "K-means begin with clusters: 40\n",
      "SC_score based on KMeans Model: 0.13854478139444687\n",
      "K-means begin with clusters: 50\n",
      "SC_score based on KMeans Model: 0.1688668768773385\n",
      "K-means begin with clusters: 60\n",
      "SC_score based on KMeans Model: 0.14393785159115624\n",
      "K-means begin with clusters: 70\n",
      "SC_score based on KMeans Model: 0.15035951742273232\n",
      "K-means begin with clusters: 80\n",
      "SC_score based on KMeans Model: 0.077653397355991\n",
      "K-means begin with clusters: 90\n",
      "SC_score based on KMeans Model: 0.14117825863972813\n",
      "K-means begin with clusters: 100\n",
      "SC_score based on KMeans Model: 0.11928034219752091\n"
     ]
    }
   ],
   "source": [
    "# 设置超参数（聚类数目K）搜索范围，此处选取的K值与作业要求中的多取了K=5\n",
    "SC_scores = []\n",
    "Ks = [5,10,20,30,40,50,60,70,80,90,100]\n",
    "for K in Ks:\n",
    "    ch = K_cluster_analysis(K, df)\n",
    "    SC_scores.append(ch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb7f9710>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/LnDJJdOd0LVJeJttoFu30sZrZiXhpFs3F02s8Tp3TqXbbbdN3RHp2d7cJHzR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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcd284e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制不同聚类数目的模型的性能，找到最佳模型／参数（分数最高）\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.title(\"silhouette_score values gained from different K values with K_Means models\")\n",
    "plt.plot(Ks, np.array(SC_scores), 'r-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "由于轮廓系数表示的是各样本点的{（类间散度-类内散度）/两者最大值}均值，故从步骤7中得到的K取不同值下的轮廓系数大小与步骤8中的曲线图可知，在所取范围内，K=5有最佳聚类效果。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
